The integration of Internet of Things (IoT) technology into renewable energy systems has revolutionized predictive maintenance, resulting in improved operational efficiency and reduced downtime. This project's objective is to use advanced feature selection, classification, and data preparation techniques to build a robust IoT-enabled predictive maintenance platform. Cleaning and normalizing sensor data is the first step in the proposed procedure to ensure data consistency and integrity. Principal Component Analysis (PCA) reduces dimensionality while maintaining crucial information when choosing features from high-dimensional IoT data streams. For categorization, the Random Forest approach is employed, which provides improved precision and interpretability in determining maintenance requirements. The approach demonstrated outstanding prediction performance and the ability to proactively identify maintenance requirements when validated using real-world statistics from renewable energy installations. The results demonstrate how combining IoT and machine learning may improve system reliability and optimize energy production, leading to smarter, greener energy solutions. © 2025 Elsevier B.V., All rights reserved.